# Loading necessary libraries
library(tidyverse)
library(Seurat)
library(viridis)
library(patchwork)
library(DropletUtils)
library(future)
library(tibble)
library(ComplexHeatmap)
library(GEOquery)
# Additional color scales
library(MetBrewer)
library(NatParksPalettes)
# Parallel processing setup
plan("multicore", workers = 80)
options(future.globals.maxSize = 30 * 1024^3) # 30 GB
# Custom themes and scales for plots
mytheme <- theme_minimal() +
theme(axis.line = element_line(),
axis.ticks = element_line(),
text = element_text(family = "Helvetica"))
simple <- NoAxes() + NoLegend()
mysc <- scale_color_viridis(option = "A")
region.pal <- c("#5EBFA2", "#F69663", "#731DD8", "#FB7C7E")
# List of sex-specific genes
sex.genes <- c("TTTY14", "NLGN4Y", "USP9Y", "UTY", "XIST", "RPS4X", "TMSB4X", "TSIX")
#levels for some metadata categories:
age.levels <- c("23GW", "14d", "33d", "54d", "2y", "3y", "13y", "27y", "50y", "51y", "79y")
age.group.levels <- c("Fetal (23GW)", "Infant (14d-54d)", "Toddler (2y-3y)", "Teen (13y)", "Adult (27y-79y)")
region.levels <- c("Germinal Zone", "Embryonic EC", "Migratory Stream", "Postnatal EC")
#Creating seurat objects from count matrices Count matrices and metadata are downloaded from GEO and saved in a folder named “matrices”.
download.file("https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE199762&format=file&file=GSE199762%5Fsamples%5Ffrom%5FGSE186538%2Etar%2Egz",
method = "curl",
"matrices/franjic_et_al_samples.tar.gz")
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srt_objects <- list()
for (s in c(gsm_samples$samplenames, "hsb231", "hsb237", "hsb628")) {
cat(paste0("Importing sample ", s, "\n"))
matrix <- ReadMtx(mtx = paste0("matrices/", s, "_counts.mtx"),
features = paste0("matrices/", s, "_genes.tsv"), feature.column = 1,
cells = paste0("matrices/", s, "_barcodes.tsv"))
metadata <- read.csv(paste0("matrices/", s, "_metadata.csv"), row.names = 1)
srt_objects[[s]] <- CreateSeuratObject(counts = matrix, meta.data = metadata)
}
Importing sample CGE
Importing sample MGE
Importing sample LGE
Importing sample EC_Stream
Importing sample dEC
Importing sample H71
Importing sample H31
Importing sample H37
Importing sample H48-g1
Importing sample H48-g2
Importing sample H39-g1
Importing sample H39-g2
Importing sample H46-g1
Importing sample H46-g2
Importing sample H29-g1
Importing sample H29-g2
Importing sample H33-g1
Importing sample H33-g2
Importing sample hsb231
Importing sample hsb237
Importing sample hsb628
Merging samples in a single Seurat object
all.exp = merge(srt_objects[[1]], srt_objects[-1])
all.exp@meta.data$region = factor(all.exp@meta.data$region, region.levels)
all.exp@meta.data$age = factor(all.exp@meta.data$age, levels = age.levels)
all.exp@active.assay = "RNA"
all.exp = all.exp %>%
NormalizeData(assay = "RNA",
verbose = F) %>%
FindVariableFeatures() %>%
ScaleData()
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix
VariableFeatures(all.exp@assays$RNA) = all.exp@assays$RNA@var.features[!(all.exp@assays$RNA@var.features %in% sex.genes)]
all.exp = all.exp %>% RunPCA(npcs = 50)
PC_ 1
Positive: NEAT1, B2M, IGFBP7, SLC1A3, CLDN5, ATP1A2, FLT1, MT2A, EPAS1, HLA-E
MECOM, VIM, LEF1, IFITM3, APOE, MYO10, CLEC3B, ID1, ABCG2, PREX2
ITM2A, FLI1, CST3, IFI27, ITPR2, FN1, ID3, MTUS1, CGNL1, ITIH5
Negative: RALYL, RYR2, KCNQ5, STXBP5L, KHDRBS2, LINGO2, FRMPD4, PTPRR, MIR137HG, GRM7
CNTNAP5, KCNB2, SNAP25, ZNF385B, HCN1, CHRM3, ASIC2, SV2B, CDH18, OLFM3
AL008633.1, CDH12, MLIP, ST6GALNAC5, GRM1, CACNG3, LINC01250, CNTN5, EPHA6, CHSY3
PC_ 2
Positive: FLT1, CLDN5, COBLL1, MECOM, LEF1, ABCB1, ARHGAP29, ADGRF5, ABCG2, EGFL7
PODXL, ERG, EPAS1, SLC7A5, EBF1, JCAD, CLEC3B, PTPRB, ITIH5, PRKCH
PECAM1, IFI27, ITM2A, KLF2, ITGA1, VWF, LINC02147, ST8SIA6, FN1, TBX3
Negative: ERBB4, SOX2-OT, ST18, PLP1, TMEM144, UGT8, ZNF536, TF, SOX6, DOCK10
MBP, AL589740.1, MOBP, LINC00609, BCAS1, DOCK5, CRYAB, MOG, CNP, ANLN
CERCAM, CNDP1, ENPP2, LPAR1, SCD, LINC01608, NKX6-2, PLEKHH1, FA2H, CLDN11
PC_ 3
Positive: ADGRV1, GLIS3, CFAP47, CFAP54, CFAP157, ID4, AQP4, DTHD1, ADGB, BMPR1B
AC104078.2, CFAP73, CFAP299, WDR49, TCTEX1D1, CFAP43, IQGAP2, DNAAF1, CFAP52, ARMC3
DCDC1, VWA3A, SPATA17, CCDC173, SPAG17, GFAP, STK33, LRRIQ1, LRRC9, TTC29
Negative: MBP, PLP1, ST18, MOBP, RNF220, SLC24A2, TF, ENPP2, TMEM144, UGT8
MOG, CNP, CLDN11, DOCK10, SPOCK3, AC012494.1, DBNDD2, ERMN, PLEKHH1, NKX6-2
DOCK5, C10orf90, LINC01608, MAG, FRMD4B, CNDP1, CDK18, ANLN, SLCO1A2, OPALIN
PC_ 4
Positive: GAD1, DLX6-AS1, NXPH1, GAD2, GRIK1, LHFPL3, ADARB2, ZNF385D, VWC2, ERBB4
IGF1, AC068308.1, SOX4, AC125613.1, MAF, SOX11, ST8SIA4, KIF26B, PTCHD4, KCNC2
SLC6A1-AS1, ELAVL2, FSTL5, ZNF804A, AC117461.1, SLC6A1, EGFR, SOX6, SDK1, PTPRM
Negative: CFAP157, DNAAF1, CFAP299, DTHD1, ADGB, CFAP73, AC104078.2, DNAH12, PLP1, CFAP52
ST18, MOBP, RNF220, CFAP43, TCTEX1D1, TTC29, ENPP2, TMEM144, ARMC3, TF
CAPS, SLC47A2, ROPN1L, PEX5L, RSPH1, MBP, WDR63, LINC02416, SPAG8, MOG
PC_ 5
Positive: ERBB4, GAD1, SLC6A1, NXPH1, ADARB2, SOX2-OT, GAD2, DLX6-AS1, SLC6A1-AS1, ZNF536
GRIK1, SPOCK3, VWC2, LHFPL3, ZNF385D, KCNMB2-AS1, KCNC2, SOX6, PLD5, SHISA6
BTBD11, PTCHD4, AC125613.1, RBMS3-AS3, FSTL5, ALK, THSD7A, SYNPR, LINC00200, KIF26B
Negative: ADAM28, DOCK8, APBB1IP, CSF1R, TBXAS1, PTPRC, FYB1, SLC11A1, LNCAROD, SYK
RUNX1, TLR2, CSF3R, MS4A7, RBM47, C3, SAMSN1, AL163541.1, AC131944.1, CSF2RA
IKZF1, CD86, SLC2A5, CD74, INPP5D, C1QB, MSR1, LRMDA, CPVL, RGS1
all.exp = all.exp %>%
RunUMAP(dims = 1:22) %>%
FindNeighbors(dims = 1:22) %>%
FindClusters(resolution = c(0.8, seq(0.5, 2, 0.5)))
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session14:33:42 UMAP embedding parameters a = 0.9922 b = 1.112
14:33:42 Read 124917 rows and found 22 numeric columns
14:33:42 Using Annoy for neighbor search, n_neighbors = 30
14:33:42 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:33:58 Writing NN index file to temp file /tmp/RtmpH6686P/filee354748b0334b
14:33:58 Searching Annoy index using 80 threads, search_k = 3000
14:34:00 Annoy recall = 100%
14:34:01 Commencing smooth kNN distance calibration using 80 threads with target n_neighbors = 30
14:34:05 Initializing from normalized Laplacian + noise (using irlba)
14:34:30 Commencing optimization for 200 epochs, with 5654058 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:35:37 Optimization finished
Computing nearest neighbor graph
Computing SNN
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
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[----|----|----|----|----|----|----|----|----|----|
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[----|----|----|----|----|----|----|----|----|----|
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 124917
Number of edges: 4505161
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9526
Number of communities: 44
Elapsed time: 39 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 124917
Number of edges: 4505161
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9633
Number of communities: 34
Elapsed time: 37 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 124917
Number of edges: 4505161
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9471
Number of communities: 50
Elapsed time: 38 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 124917
Number of edges: 4505161
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9349
Number of communities: 59
Elapsed time: 44 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 124917
Number of edges: 4505161
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9240
Number of communities: 69
Elapsed time: 38 seconds
FeaturePlot(all.exp, c("GFAP", "HOPX", "TOP2A", "EOMES", "DCX", "TBR1", "SLC17A7", "GAD2", "LHX6", "NR2F2", "PROX1", "CALB2", "LAMP5", "RELN", "VIP", "NPY", "SST", "OLIG2", "SOX10", "MBP", "FOXJ1", "PECAM1", "PDGFRB", "ADAM28"), order = F, raster = F, ncol = 6) &
simple &
mysc &
coord_fixed()
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
ggsave("all.exp_featplots.png", width = 10, height = 6, scale = 2)
Main Cell Types
DimPlot(all.exp, raster = F, label = T, repel = T, group.by = "all.exp_type", shuffle = T) +
coord_fixed() +
simple +
labs(title = "Cell Types")
Unsupervised Clustering
DimPlot(all.exp, raster = F, label = T, group.by = "RNA_snn_res.1", shuffle = T) +
coord_fixed() +
simple+
scale_color_manual(values = met.brewer("VanGogh2", n = 50, override.order = T))+
labs(title = "Unsupervised Clustering")
Donor Age
DimPlot(all.exp, raster = F, label = F, group.by = "age", shuffle = T) +
coord_fixed() +
NoAxes() +
scale_color_viridis_d(option = "H", name = "Donor Age")+
labs(title = NULL)
Donor Age Group
DimPlot(all.exp, raster = F, label = F, group.by = "age_group", shuffle = T) +
coord_fixed() +
NoAxes() +
scale_color_manual(values=met.brewer("Hokusai3"), name = "Donor Age Group")+
labs(title = NULL)
Sample ID
DimPlot(all.exp, raster = F, label = F, group.by = "sample", shuffle = T) +
coord_fixed() +
NoAxes() +
scale_color_manual(values=met.brewer("Juarez", 16), name = "Sample")+
labs(title = NULL)
Sample Origin
DimPlot(all.exp, raster = F, label = F, group.by = "region", shuffle = T) +
coord_fixed() +
NoAxes() +
scale_color_manual(name = "Sample Origin", values = region.pal) +
labs(title = NULL)
EC Stream Cells
DimPlot(all.exp, group.by = "stream_highlight", order = T, raster = F) +
scale_color_manual(values = (region.pal)[3], na.value = "grey85", labels = c("EC Stream (14d)", "Other cells")) +
coord_fixed() +
NoAxes() +
labs(title = "EC Stream Cells")
Nuclear Fraction
FeaturePlot(all.exp, "nuclear_fraction", order = T, raster = F) +
scale_color_viridis(limits = c(0, 1), name = "Nuclear Fraction") +
coord_fixed() +
NoAxes() +
labs(title = NULL)
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Idents(all.exp) = "all.exp_type"
all.exp <- BuildClusterTree(all.exp, assay = "RNA", reorder = T, features = all.exp@assays$RNA@var.features)
Reordering identity classes and rebuilding tree
saveRDS(all.exp, file = "all.exp.rds", compress = F)
inter.exp.cells = all.exp@meta.data %>% filter(all.exp_type == "Cortical Interneurons") %>% rownames()
DimPlot(all.exp, label = T, cells.highlight = inter.exp.cells, raster = F) + simple + coord_fixed()
inter.exp = subset(all.exp, cells = inter.exp.cells)
load("reorder_index.Rdata") # load the index for reordering the cells. In addition to using the same seed (42, the default ones in Seurat), in order to reproduce the exact same PCA results and UMAP projection, our count matrices should be in the same order as the original ones. For the sake of reproducibility, we will reorder the cells.
inter.exp@assays$RNA@counts <- inter.exp@assays$RNA@counts[ , reorder_index]
inter.exp@assays$RNA@data <- inter.exp@assays$RNA@data[ , reorder_index]
inter.exp@active.assay = "RNA"
inter.exp = inter.exp %>%
NormalizeData(assay = "RNA",
verbose = F) %>%
FindVariableFeatures() %>%
ScaleData(features = rownames(.))
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix
VariableFeatures(inter.exp@assays$RNA) = inter.exp@assays$RNA@var.features[!(inter.exp@assays$RNA@var.features %in% sex.genes)]
inter.exp@active.assay = "RNA"
inter.exp = inter.exp %>% RunPCA(npcs = 20) %>%
RunUMAP(dims = 1:8) %>%
FindNeighbors(dims = 1:8) %>%
FindClusters(resolution = c(0.8, seq(0.5, 2, 0.5)))
PC_ 1
Positive: CASC15, SOX4, SOX11, SYNE2, SOX2-OT, ZBTB20, CHD7, AC125613.1, HBG2, NBAT1
KCNH8, VIM, AC068308.1, CECR2, RPS11, DLEU2, NHSL1, SPP1, AC061958.1, HBA2
AC090531.1, RFTN2, HBA1, PDZRN4, ZBTB20-AS5, HBG1, NKAIN3, HIST1H2AC, VCAN-AS1, ENO4
Negative: CSMD1, OXR1, AGBL4, KCNC2, PCLO, LRRC4C, ZNF385D, ASTN2, SPOCK3, LRP1B
ATP1B1, GALNTL6, PLCB1, RYR2, GRIK1, CHRM3, KCND2, CADM2, CDH9, MT-CO2
LINC-PINT, PTPRG, HCN1, MCTP1, KCTD16, KHDRBS2, LRRTM4, RASGRF2, PEG3, PTPRM
PC_ 2
Positive: SOX6, KIAA1217, SATB1, SATB1-AS1, PLCH1, GPC6, PRKG1, GRIK3, MYO5B, ST6GALNAC5
TENM1, RSPO2, RASGRF2, RAPGEF5, GRIA3, KLHL5, CRHBP, SPARCL1, WLS, PAWR
SLIT2, TAC1, ELAVL2, HGF, XYLT1, MMP16, ST8SIA4, CNTNAP3B, ENOX1, PTCHD4
Negative: ADARB2, DSCAM, PROX1, KCNT2, DOCK10, AC013265.1, VIP, CALB2, LINGO2, GALNT13
CXCL14, PDE3A, LAMA3, CCK, ADRA1B, AL391832.4, EGFR, CNR1, LINC00200, CRH
ARPP21, SORCS3, PRR16, CCDC85A, CCNH, SEZ6L, PCSK2, THSD7A, FSTL5, INPP4B
PC_ 3
Positive: EYA4, LINC00299, LAMP5, KIT, SV2C, TRPC3, FBXL7, FGF13, AC132803.1, SGK1
PRELID2, TMEM132D, CACNA2D1, MYO16, UNC5C, NTNG1, PTPRT, ALK, HAPLN1, PDGFD
AC137770.1, PDZD2, TPD52L1, GRIN2A, LINC01344, LINC00298, GRIA4, SGCZ, CHST9, POU6F2
Negative: ROBO1, CACNA2D3, NELL1, CDH10, SYNPR, PDE4B, KCNMB2-AS1, GRM1, VIP, GRID2
AC091885.2, AC013265.1, AC090579.1, RGS6, TRHDE, OXR1, AC117461.1, ROBO2, CHST15, GRM7
CALB2, KIAA1217, CNTN3, ASIC2, OLFM3, GRIK3, ASIC4, CHRM3, SHISA6, CHRNA2
PC_ 4
Positive: SLC8A1-AS1, AC023590.1, CHRM3-AS2, ARL17A, AC005064.1, AC073525.1, AL353784.1, AC005400.1, CTNNA3, AC098617.1
RFX4, GRM5-AS1, AC016042.1, AL390783.1, AL137009.1, AC016642.1, LINC00200, AC027288.3, TTN, AC096576.3
AC096576.2, GRIK1-AS1, FILIP1L, RBMS3-AS3, SCN1A-AS1, FGF12-AS1, GNG12-AS1, AC010974.2, AC092939.1, AP001825.1
Negative: MT-ATP6, MT-ND4, MT-CO2, MT-ND1, MT-CO3, MT-CYB, MT-ND3, MT-ND2, BTBD11, AC006148.1
ZNF804A, STXBP5-AS1, MT-ATP8, LINC-PINT, MT-CO1, PEG3, ASIC2, TMEM132C, MT-ND5, MT-ND4L
RGS5, NDST3, ALDOC, PLCXD3, CNTNAP3B, TAFA2, CPLX1, LRP1B, OXR1, TRPC4
PC_ 5
Positive: DPP10-AS3, AC093610.1, DPP10, DPP10-AS1, ZNF804A, PLXNA4, ZPBP, CNTNAP3B, TAFA4, TMEM132C
TAFA2, SCN1A-AS1, AP003464.1, CPED1, ADAMTS17, COL12A1, AC073525.1, NDST3, AC139720.1, EDIL3
C1QL1, AC016042.1, SLC9A9, LRRC4C, BTBD11, HS6ST3, PPARGC1A, PLCL1, AC090138.1, TRPC4
Negative: RALYL, CACNA2D3, TRHDE, NETO1, ROBO2, GRM1, GRID2, PDE1A, GRIK1, RELN
SYNPR, UNC13C, ROBO1, GRIN3A, MAN1A1, SST, SLC8A1, PDE8B, CDH8, PAWR
SHISA6, RGS6, TMTC2, TMEFF2, MTUS2, COL25A1, PDE1C, MAP3K5, GRIN2A, KLF5
14:50:27 UMAP embedding parameters a = 0.9922 b = 1.112
14:50:27 Read 20470 rows and found 8 numeric columns
14:50:27 Using Annoy for neighbor search, n_neighbors = 30
14:50:27 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:50:29 Writing NN index file to temp file /tmp/RtmpH6686P/filee3547188875cc
14:50:29 Searching Annoy index using 80 threads, search_k = 3000
14:50:29 Annoy recall = 100%
14:50:31 Commencing smooth kNN distance calibration using 80 threads with target n_neighbors = 30
14:50:33 Initializing from normalized Laplacian + noise (using irlba)
14:50:34 Commencing optimization for 200 epochs, with 829100 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:50:43 Optimization finished
Computing nearest neighbor graph
Computing SNN
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
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**************************************************|
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20470
Number of edges: 643223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8926
Number of communities: 21
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20470
Number of edges: 643223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9126
Number of communities: 14
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20470
Number of edges: 643223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8819
Number of communities: 24
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20470
Number of edges: 643223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8592
Number of communities: 28
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 20470
Number of edges: 643223
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8405
Number of communities: 37
Elapsed time: 2 seconds
Idents(inter.exp) = "RNA_snn_res.0.5"
inter.exp = BuildClusterTree(inter.exp,
dims = 1:8,
assay = "RNA",
reorder = T,
features = inter.exp@assays$RNA@var.features)
| | 0 % ~calculating
|++++ | 7 % ~01s
|++++++++ | 14% ~00s
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|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Reordering identity classes and rebuilding tree
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
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|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
inter.exp@meta.data$RNA_snn_res.0.5 = factor(inter.exp@meta.data$RNA_snn_res.0.5 , levels = inter.exp@tools$BuildClusterTree$tip.label)
inter.exp@reductions$umap@cell.embeddings[, "UMAP_1"] = inter.exp@reductions$umap@cell.embeddings[, "UMAP_1"] * -1 #Inverting the x axis so we can have more intuitive visualizations, with maturation going from left to right.
DimPlot(inter.exp, label = T, group.by = "RNA_snn_res.0.5") +
scale_color_manual(values = met.brewer("VanGogh2", length(levels(inter.exp$RNA_snn_res.0.5)))) +
simple +
coord_fixed() +
labs(title = "Unsupervised Clustering")
DimPlot(inter.exp, label = F, group.by = "age", shuffle = T) +
coord_fixed() +
NoAxes() +
scale_color_viridis_d(name = "Age", option = "H")+
labs(title = NULL)
DimPlot(inter.exp, label = F, group.by = "age_group", shuffle = T) +
coord_fixed() +
NoAxes() +
#scale_color_viridis_d()
scale_color_manual(values=met.brewer("Hokusai3"))+
labs(title = NULL)
DimPlot(inter.exp, label = F, group.by = "sample", shuffle = T) +
coord_fixed() +
NoAxes() +
scale_color_manual(values=met.brewer("Juarez", 16))+
labs(title = NULL)
DimPlot(inter.exp, raster = F, label = F, group.by = "region", shuffle = T) +
coord_fixed() +
NoAxes() +
scale_color_manual(name = "Region of Origin", values = region.pal)+
labs(title = NULL)
DimPlot(inter.exp, group.by = "stream_highlight", order = T) +
scale_color_manual(values = (region.pal)[3], na.value = "grey85") +
coord_fixed() +
NoAxes()+
labs(title = "EC Stream Cells")
DimPlot(inter.exp, group.by = "dec_highlight", order = T) +
scale_color_manual(values = (region.pal)[2], na.value = "grey85") +
coord_fixed() +
NoAxes()+
labs(title = "Embryonic EC Cells")
inter.exp@active.assay = "RNA"
(FeaturePlot(inter.exp, c("GFAP", "TNC", "SOX2", "TOP2A", "OLIG2", "SOX10", "DCX", "GABRB2", "LHX6", "NR2F2", "PROX1", "PBX3", "SST", "PVALB", "NPY", "CALB2", "VIP", "RELN", "KIT", "LAMP5", "TBR1", "SLC17A7", "ADAM28", "PECAM1"), order = T, ncol = 6) &
simple &
mysc &
coord_fixed() &
theme(text = element_text(family = "Helvetica")))
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
#ggsave("inter.exp_overview_featplots.png", height = 14, width = 28)
saveRDS(inter.exp, "inter.exp_step1.rds", compress = F)
These were the processing steps to generate the basic datasets used in the paper: all.exp and inter.exp
sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] NatParksPalettes_0.2.0 MetBrewer_0.2.0 GEOquery_2.66.0 ComplexHeatmap_2.14.0
[5] future_1.32.0 DropletUtils_1.18.1 SingleCellExperiment_1.20.1 SummarizedExperiment_1.28.0
[9] Biobase_2.58.0 GenomicRanges_1.50.2 GenomeInfoDb_1.34.9 IRanges_2.32.0
[13] S4Vectors_0.36.2 BiocGenerics_0.44.0 MatrixGenerics_1.10.0 matrixStats_1.0.0
[17] patchwork_1.1.2 viridis_0.6.3 viridisLite_0.4.2 SeuratObject_4.1.3
[21] Seurat_4.3.0.1 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[25] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[29] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] utf8_1.2.3 spatstat.explore_3.2-1 reticulate_1.30 R.utils_2.12.2 tidyselect_1.2.0
[6] htmlwidgets_1.6.2 BiocParallel_1.32.6 Rtsne_0.16 munsell_0.5.0 ragg_1.2.5
[11] codetools_0.2-19 ica_1.0-3 miniUI_0.1.1.1 withr_2.5.0 spatstat.random_3.1-5
[16] colorspace_2.1-0 progressr_0.13.0 knitr_1.43 rstudioapi_0.14 ROCR_1.0-11
[21] tensor_1.5 listenv_0.9.0 labeling_0.4.2 GenomeInfoDbData_1.2.9 polyclip_1.10-4
[26] farver_2.1.1 rhdf5_2.42.1 parallelly_1.36.0 vctrs_0.6.3 generics_0.1.3
[31] xfun_0.39 timechange_0.2.0 R6_2.5.1 doParallel_1.0.17 clue_0.3-64
[36] locfit_1.5-9.8 cachem_1.0.8 bitops_1.0-7 rhdf5filters_1.10.1 spatstat.utils_3.0-3
[41] DelayedArray_0.24.0 promises_1.2.0.1 scales_1.2.1 gtable_0.3.3 beachmat_2.14.2
[46] globals_0.16.2 goftest_1.2-3 rlang_1.1.1 systemfonts_1.0.4 GlobalOptions_0.1.2
[51] splines_4.2.3 lazyeval_0.2.2 spatstat.geom_3.2-1 yaml_2.3.7 reshape2_1.4.4
[56] abind_1.4-5 httpuv_1.6.11 tools_4.2.3 ellipsis_0.3.2 jquerylib_0.1.4
[61] RColorBrewer_1.1-3 ggridges_0.5.4 Rcpp_1.0.10 plyr_1.8.8 sparseMatrixStats_1.10.0
[66] zlibbioc_1.44.0 RCurl_1.98-1.12 deldir_1.0-9 pbapply_1.7-2 GetoptLong_1.0.5
[71] cowplot_1.1.1 zoo_1.8-12 ggrepel_0.9.3 cluster_2.1.4 magrittr_2.0.3
[76] data.table_1.14.8 scattermore_1.2 circlize_0.4.15 lmtest_0.9-40 RANN_2.6.1
[81] fitdistrplus_1.1-11 hms_1.1.3 mime_0.12 evaluate_0.21 xtable_1.8-4
[86] gridExtra_2.3 shape_1.4.6 compiler_4.2.3 KernSmooth_2.23-20 crayon_1.5.2
[91] R.oo_1.25.0 htmltools_0.5.5 later_1.3.1 tzdb_0.3.0 MASS_7.3-58.2
[96] Matrix_1.5-3 cli_3.6.1 R.methodsS3_1.8.2 parallel_4.2.3 igraph_1.5.0
[101] pkgconfig_2.0.3 sp_2.0-0 plotly_4.10.2 scuttle_1.8.4 spatstat.sparse_3.0-2
[106] xml2_1.3.4 foreach_1.5.2 bslib_0.5.0 dqrng_0.3.0 XVector_0.38.0
[111] digest_0.6.32 sctransform_0.3.5 RcppAnnoy_0.0.20 spatstat.data_3.0-1 rmarkdown_2.22
[116] leiden_0.4.3 uwot_0.1.16 edgeR_3.42.2 DelayedMatrixStats_1.20.0 shiny_1.7.4
[121] rjson_0.2.21 lifecycle_1.0.3 nlme_3.1-162 jsonlite_1.8.7 Rhdf5lib_1.20.0
[126] limma_3.54.2 fansi_1.0.4 pillar_1.9.0 lattice_0.20-45 fastmap_1.1.1
[131] httr_1.4.6 survival_3.5-3 glue_1.6.2 png_0.1-8 iterators_1.0.14
[136] sass_0.4.6 stringi_1.7.12 HDF5Array_1.26.0 textshaping_0.3.6 ape_5.7-1
[141] irlba_2.3.5.1 future.apply_1.11.0